Transferable Water Potentials Using Equivariant Neural Networks

Maxson, Tristan; Szilvási, Tibor

doi:10.1021/acs.jpclett.4c00605

Title: Transferable Water Potentials Using Equivariant Neural Networks

Journal Article · 27 March 2024 · Journal of Physical Chemistry Letters

DOI: https://doi.org/10.1021/acs.jpclett.4c00605 · OSTI ID:2341237

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University of Alabama, Tuscaloosa, AL (United States); The University of Alabama
University of Alabama, Tuscaloosa, AL (United States)

Machine learning interatomic potentials (MLIPs) have emerged as a technique that promises quantum theory accuracy for reduced cost. It has been proposed that MLIPs trained on solely liquid water data cannot accurately transfer to the vapor–liquid equilibrium while recovering the many-body decomposition (MBD) analysis of gas-phase water clusters. This suggests that MLIPs do not directly learn the physically correct interactions of water molecules, limiting transferability. In this work, we show that MLIPs using equivariant architecture and trained on 3200 liquid water structures reproduces liquid-phase water properties (e.g., density within 0.003 g/cm³ between 230 and 365 K), vapor–liquid equilibrium properties up to 550 K, the MBD analysis of gas-phase water cluster up to six-body interactions, and the relative energy and the vibrational density of states of ice phases. Here, we show that potentials developed using equivariant MLIPs allow transferability for arbitrary phases of water that remain stable in nanosecond long simulations.

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Research Organization:: University of Alabama, Tuscaloosa, AL (United States)

Sponsoring Organization:: USDOE Office of Science (SC), Advanced Scientific Computing Research (ASCR); USDOE Office of Science (SC), Basic Energy Sciences (BES); National Science Foundation (NSF)

Grant/Contract Number:: SC0024654; SC0023112; AC02-05CH11231

OSTI ID:: 2341237

Journal Information:: Journal of Physical Chemistry Letters, Journal Name: Journal of Physical Chemistry Letters Journal Issue: 14 Vol. 15; ISSN 1948-7185

Publisher:: American Chemical SocietyCopyright Statement

Country of Publication:: United States

Language:: English

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37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CHEMISTRY
Chemical structure
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